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. 2024 Jan 6;24(1):28.
doi: 10.1186/s12877-023-04593-8.

Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation

Rui Li #  1 , Ying Hui #  1 , Xiaoyue Zhang #  2 , Shun Zhang  3 , Bin Lv  2 , Yuan Ni  2 , Xiaoshuai Li  1 , Xiaoliang Liang  3 , Ling Yang  4 , Han Lv  1 , Zhiyu Yin  5 , Hongyang Li  6 , Yingping Yang  7 , Guangfeng Liu  8 , Jing Li  9 , Guotong Xie  10 , Shouling Wu  11 , Zhenchang Wang  12
Affiliations

Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation

Rui Li et al. BMC Geriatr. .

Abstract

Background: The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition.

Methods: We included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P < 0.05 was considered statistically significant. The correlation between the CI and RMPs were explored, then the correlation coefficients between CI and RMPs were analyzed. Random Forest nonlinear classification model was used to predict whether one having cognitive decline or not. The assessment criterion was the AUC value derived from the working characteristic curve.

Results: The fractal dimension (FD) and global vein width were significantly correlated with the CI (P < 0.05). Age (0.193), BMI (0.154), global vein width (0.106), retinal vessel FD (0.099), and CRAE (0.098) were the variables in this model that were ranked in order of feature importance. The AUC values of the model were 0.799.

Conclusions: Establishment of a predictive model based on the extraction of vascular features from fundus images has a high recognizability and predictive power for cognitive function and can be used as a screening method for CI.

Keywords: Cognitive function; Deep learning segmentation; Fundus photography; Retinal microvascular parameters.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of patient inclusion
Fig. 2
Fig. 2
Examples of retinal vessel segmentation results, a and b are images without cognitive impairment, c and d are images with cognitive impairment. Examples of retinal vessel segmentation results. a, c Original fundus image. b, d Segmented arteries and veins: arteries shown in red, and veins in blue
Fig. 3
Fig. 3
Model performance on test set. a shows the receiver operating characteristic curves with black line representing our model and red dashed line representing the random guess. and b shows the normalized confusion matrices of our model
Fig. 4
Fig. 4
Performance of the model with physiological indexes alone on test set. a shows the receiver operating characteristic curves with black line representing our model and red dashed line representing the random guess. And (b) shows the normalized confusion matrices of our model
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